Data engineering has become the backbone of modern organizations, enabling the storage, transformation, and delivery of high-quality data to power analytics and machine learning initiatives. With the exponential growth of data volume and complexity, mastering scalable and efficient frameworks is essential for data-driven decision-making. This course is designed to provide professionals with the technical expertise required to build, optimize, and manage data pipelines using Python and PySpark.
The program begins with an overview of the core concepts of data engineering, including distributed processing, data modeling, and the role of transformation in preparing datasets for business intelligence and advanced analytics. It then introduces Python as a flexible and powerful language for data engineering tasks and demonstrates how PySpark provides the scalability required for handling massive datasets in real-world enterprise environments.
Throughout the course, participants will gain hands-on experience with practical data engineering tasks such as data ingestion, cleansing, enrichment, and transformation, while leveraging PySpark’s distributed computing capabilities. Learners will also explore best practices for working with structured and unstructured data, building optimized data pipelines, and ensuring high levels of reliability and performance.
The course emphasizes applied learning by incorporating real-world case studies across finance, healthcare, e-commerce, and other industries where scalable data processing is critical. Participants will design and deploy pipelines that demonstrate not only technical skill but also business relevance, ensuring alignment between engineering and strategic objectives.
Finally, the training covers emerging topics, such as PySpark integration with cloud platforms, leveraging Python libraries for machine learning preprocessing, and optimizing workflows for data lakes and data warehouses. By the end of the course, participants will be equipped to design, build, and manage high-performing pipelines that unlock actionable insights from complex datasets.
Who Should Attend
- Data engineers working with large-scale datasets.
- Python developers transitioning into data engineering roles.
- Data architects building distributed systems.
- BI developers seeking to improve data transformation skills.
- Cloud engineers managing data workflows.
- Machine learning engineers needing optimized pipelines.
- Data scientists requiring scalable preprocessing solutions.
- IT professionals overseeing enterprise data systems.
- Technical consultants in data and analytics.
- Project managers supervising big data initiatives.
Duration
5 days
Course Objectives
By completing this training, participants will be able to:
- Understand the fundamentals of data engineering with Python and PySpark.
- Design and develop distributed data pipelines for real-world use cases.
- Perform large-scale data ingestion, transformation, and optimization.
- Apply advanced PySpark techniques for data manipulation.
- Integrate Python libraries with PySpark for enhanced functionality.
- Ensure data quality, consistency, and reliability in pipelines.
- Leverage PySpark for structured and unstructured data processing.
- Deploy scalable pipelines on cloud platforms and data lakes.
- Optimize performance for enterprise-level data systems.
- Align data engineering solutions with business and analytics goals.
Comprehensive Course Outline
Module 1: Introduction to Data Engineering with Python and PySpark
- Overview of modern data engineering practices.
- Python for data engineering: strengths and applications.
- Distributed computing and PySpark fundamentals.
- Setting up the Python and PySpark development environment.
Module 2: Core Concepts of PySpark
- PySpark architecture and RDDs (Resilient Distributed Datasets).
- Working with DataFrames and Datasets.
- Lazy evaluation and transformations vs. actions.
- Best practices for PySpark development.
Module 3: Data Ingestion Techniques
- Reading data from structured sources (CSV, JSON, Parquet).
- Ingesting unstructured and semi-structured data.
- Connecting to databases and APIs with PySpark.
- Handling streaming data with Spark Structured Streaming.
Module 4: Data Transformation with PySpark
- Cleaning and preprocessing large datasets.
- Advanced joins, aggregations, and window functions.
- Data enrichment and feature engineering.
- Handling schema evolution and data consistency.
Module 5: Optimizing Data Pipelines
- Partitioning and bucketing for performance.
- Caching, persistence, and query optimization.
- Managing shuffle operations and memory usage.
- Monitoring and debugging pipelines.
Module 6: Integration with Python Ecosystem
- Using Python libraries (Pandas, NumPy) with PySpark.
- Interoperability between PySpark and ML frameworks.
- Handling small vs. large datasets with hybrid approaches.
- Extending PySpark with UDFs (User-Defined Functions).
Module 7: PySpark for Streaming and Real-Time Data
- Fundamentals of Spark Structured Streaming.
- Real-time transformations and aggregations.
- Integrating PySpark with Kafka and other message queues.
- Case studies in real-time analytics.
Module 8: Data Lakes and Warehousing with PySpark
- Writing to HDFS, S3, and cloud storage.
- PySpark with Snowflake, Redshift, and BigQuery.
- Best practices for ETL in hybrid ecosystems.
- Data governance in distributed storage systems.
Module 9: Advanced Topics and Emerging Practices
- PySpark with Kubernetes and containerized deployments.
- Workflow orchestration with Airflow and PySpark.
- Preprocessing for machine learning pipelines.
- Leveraging PySpark in a data mesh architecture.
Module 10: Project and Case Studies
- Designing a complete ETL pipeline using PySpark.
- Implementing optimization strategies for scalability.
- End-to-end integration with BI and ML tools.
- Final project presentation and evaluation.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.
Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment
Unless otherwise agreed between the two parties payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better
